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most citedA Comprehensive Survey on Data Augmentation

20 citations · 31 across the 31 of their papers we have counts for

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5 papers · 1 filter

cs.LG2025

Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation

Tao Zhe, Huazhen Fang, Kunpeng Liu +3

Feature transformation enhances downstream task performance by generating informative features through mathematical feature crossing. Despite the advancements in deep learning, fea…

cs.AI2025

Dataforge: Agentic Platform for Autonomous Data Engineering

Xinyuan Wang, Hongyu Cao, Kunpeng Liu +1

The growing demand for artificial intelligence (AI) applications in materials discovery, molecular modeling, and climate science has made data preparation a critical but labor-inte…

cs.CL2025

Efficient Post-Training Refinement of Latent Reasoning in Large Language Models

Xinyuan Wang, Dongjie Wang, Wangyang Ying +5

Reasoning is a key component of language understanding in Large Language Models. While Chain-of-Thought prompting enhances performance via explicit intermediate steps, it suffers f…

cs.LG2025

Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

Dongjie Wang, Yanyong Huang, Wangyang Ying +11

Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric…

cs.LG2025

Iterative Feature Space Optimization through Incremental Adaptive Evaluation

Yanping Wu, Yanyong Huang, Zhengzhang Chen +5

Iterative feature space optimization involves systematically evaluating and adjusting the feature space to improve downstream task performance. However, existing works suffer from…